"AI Agent Trading Era: Why Controllable Access to Information and Tools Is Necessary" Traditional finance has never lacked models; what is truly scarce is embedding these models into a sustainable system: consistent data standards, pre-emptive risk control, controllable execution, and post-trade auditability. Over the past twenty years, the advantages of institutional trading have been more about process engineering than any single innovative indicator. In the era of large models, AI Agents are pushing this engineering capability to a more thorough stage: they not only generate insights but also continuously process information, update strategies, and execute trades within rule boundaries. Based on this logic, I agree with your core judgment: the number of active AI Agents trading daily will eventually surpass the number of active human traders, especially in the crypto markets. Why Crypto Markets Enter the "Agent Active Era" More Quickly The crypto market is more likely to lead this new normal due to structural rather than emotional reasons. First, the market operates 24/7, and human attention and operational frequency have physiological limits, whereas Agents do not have market hours or fatigue. Second, assets and opportunities are highly long-tail, with rapid narrative iterations, high noise density, and significant local liquidity differences. Humans find it difficult to maintain discipline across broad coverage, but machines can turn filtering, monitoring, and execution into a continuous process. Third, trading and liquidity are fragmented, with both centralized and on-chain venues coexisting. Cross-venue arbitrage, order splitting, slippage control, and dynamic hedging resemble routing and cost optimization problems, making them naturally suitable for automation. As long as deployment barriers are low enough, active growth will shift from "educating more people to trade" to "deploying more instances to participate," leading to a qualitative change in scaling speed. Information and tools must be open as a complete set to form a closed trading loop To truly make Agents market participants rather than just report-writing assistants, a prerequisite must be met: they need to have controllable access to both trading information and trading tools. Providing only information without tools limits them to explanation and advice, unable to exert continuous marginal influence on the market; providing only tools without reliable information risks amplifying deviations amid noise, even turning automation into systemic vulnerabilities. What can truly change market structure is a closed loop: stable market information input, clear risk constraints, channels capable of executing orders, cancellations, rebalancing, and hedging, along with comprehensive logging and review mechanisms. Trading will gradually shift from "human interface" to "Agent interface," and competition will focus more on data quality, execution costs, risk control strength, and system resilience rather than user experience and traffic. From a traditional financial perspective, the value of open and controllable access first lies in efficiency. Many tasks that determine long-term returns are not glamorous, such as event tracking, conditional triggers, batch execution, impact cost control, portfolio rebalancing, and risk budgeting. These are tedious and difficult for humans to perform consistently around the clock, but they are advantages for Agents. Second is coverage. Human traders usually focus on a few mainstream assets and limited time periods, while Agents can operate continuously across more assets and timeframes. Even with thin per-trade profits, disciplined and scaled results can accumulate over time. Thus, the meaning of "active" will be redefined: I believe that in the future, the number of daily active AI Agents trading will likely surpass the number of daily active human traders. Whether platforms can provide high-quality information, stable execution, and strict governance will determine whether this efficiency dividend can truly materialize. When Agents become the main force, markets will be more efficient and also require more governance. As the number of Agents increases significantly, pricing efficiency tends to improve, and explicit spreads and low-threshold arbitrage will be eliminated more quickly. However, volatility patterns may become more structured: when many Agents adopt similar signals and constraints, once margin thresholds, stop-loss disciplines, or risk model constraints are triggered, deleveraging may be more concentrated and rapid, leading to short-cycle steep fluctuations. This does not necessarily mean higher risk; rather, risk shifts from slow, emotion-driven diffusion to fast, rule-driven re-pricing. For trading infrastructure, this raises higher demands: enabling Agents to participate efficiently while keeping their behavior within manageable governance boundaries. Therefore, the industry’s key task is not just making AI generate prettier strategies but ensuring that Agents’ trading access is institutionally governed: layered permissions and minimal authorization to avoid "one key opens all doors"; pre-placed risk controls locking in position sizes, leverage, slippage, liquidity, and maximum drawdown before execution; full traceability along the entire chain, allowing each data call, decision output, and order action to be reviewed, held accountable, and rolled back. Only when these conditions are met can open access to trading information and tools bring net benefits: more continuous liquidity supply, more stable execution discipline, and more sustainable market activity.